Novel United Kingdom prognostic model for 30-day mortality following transcatheter aortic valve implantation

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Abstract

Objective Existing clinical prediction models (CPM) for short-term mortality after transcatheter aortic valve implantation (TAVI) have limited applicability in the UK due to moderate predictive performance and inconsistent recording practices across registries. The aim of this study was to derive a UK-TAVI CPM to predict 30-day mortality risk for benchmarking purposes. Methods A two-step modelling strategy was undertaken: first, data from the UK-TAVI Registry between 2009 and 2014 were used to develop a multivariable logistic regression CPM using backwards stepwise regression. Second, model-updating techniques were applied using the 2013-2014 data, thereby leveraging new approaches to include frailty and to ensure the model was reflective of contemporary practice. Internal validation was performed by bootstrapping to estimate in-sample optimism-corrected performance. Results Between 2009 and 2014, up to 6339 patients were included across 34 centres in the UK-TAVI Registry (mean age, 81.3; 2927 female (46.2%)). The observed 30-day mortality rate was 5.14%. The final UK-TAVI CPM included 15 risk factors, which included two variables associated with frailty. After correction for in-sample optimism, the model was well calibrated, with a calibration intercept of 0.02 (95% CI -0.17 to 0.20) and calibration slope of 0.79 (95% CI 0.55 to 1.03). The area under the receiver operating characteristic curve, after adjustment for in-sample optimism, was 0.66. Conclusion The UK-TAVI CPM demonstrated strong calibration and moderate discrimination in UK-TAVI patients. This model shows potential for benchmarking, but even the inclusion of frailty did not overcome the need for more wide-ranging data and other outcomes might usefully be explored.

Figures

  • Figure 1 Flow chart illustrating the steps undertaken within the two-stage modelling strategy to derive and internally validate the UK-TAVI CPM. CPM, clinical prediction model; TAVI, transcatheter aortic valve implantation.
  • Figure 2 Patient flow chart through the exclusion criteria for both the main development sample and the sensitivity analysis that modelled using 2013/2014 data only. TAVI, transcatheter aortic valve implantation.
  • Figure 3 Graphical representation of the UK-TAVI (transcatheter aortic valve implantation) clinical prediction model (CPM). First, multiply each variable (either yes/no for categorical variables or enter the observed continuous variable) by the corresponding coefficient and then sum across all variables to obtain the linear predictor. The linear predictor can then be converted to a predicted risk using the graph or through the equation: exp(Linear Predictor)/{1+exp(Linear Predictor)}. The dotted arrows show the example described in the text. BAV, balloon aortic valvuloplasty; BMI, body mass index; eGFR, estimated glomerular filtration rate; N/A, not applicable; PA, pulmonary artery; TF, transfemoral access.
  • Figure 4 Forest plot of the calibration intercept for the UK-TAVI (transcatheter aortic valve implantation) clinical prediction model (CPM) across all centres. Centres have been sorted based on the calibration intercept. Three centres with no deaths by 30 days have been removed.

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CITATION STYLE

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Martin, G. P., Sperrin, M., Ludman, P. F., De Belder, M. A., Redwood, S. R., Townend, J. N., … Mamas, M. A. (2018). Novel United Kingdom prognostic model for 30-day mortality following transcatheter aortic valve implantation. Heart, 104(13), 1109–1116. https://doi.org/10.1136/heartjnl-2017-312489

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